1. Radars in Helsinki Testbed
Elena Saltikoff, FMI

2. Tutkia tutkia – Radars to find out
Where’s precipitation How much ? 5 min, 1 km res.
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3. A radar does not measure precipitation, just scattering of microwaves
P= Measured reflected power (watts 10-13)
Dual Pol can improve QPE
by improving these
Clutter cancellation
Z=Reflectivity by precipitation (dBZ)
Assume precipitation type
Z=aRb, assume a and b
R=Rainfall intencity (mm/h)
Not so easy for gauges either
Integrated rainfall in N hours (mm)
Rainfall in river catchment area (m3)
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4. Reflectivity and velocity
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5. Total dBZ Velocity Filtered dBZ Rho
Clutter can be defined as Microwaves scattered by unwanted objects
Total dBZ Velocity Filtered dBZ Rho
Hills
Hill speed
zero m/s
No hills
Sea clutter
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6. Profile measurement volume
The bad news: we live on a spherical globe. For FMI standard products, we compensate as much as we can...
Profile measurement volume h r
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7. Weather Radars in Helsinki Testbed
Ready made datasets – gif images
dBZ: Reflectivity images every 15 minutes are composites of 4 FMI radars: Vantaa in the centre, Ikaalinen in Northwest, Korpo in Southwest and Anjalankoski in east
vpr: Vertical profiles of reflectivity separately from each above mentioned individual radar. Also as text files.
Rho and ZDR: dual polarization parameters hourly from Kumpula radar at Helsinki University Campus
Data archived as IRIS raw files (typically 2-8 Mb)
At FMI process in Jordan or in Harry
At University, process in Analysis
Possible to convert to other formats

8. Radar data parameters
FMI data is the same from all radars, all campaigns
Kumpula radar data is different for each campaign
August 2005: 5 tasks repeated every 10 minutes
Nov 2005 and May 2005: two different schedules alternating, some tasks the same all month
Tasks described in a pdf at the testbed website
Inventory of datasets available too
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9. Excercise: What is this ?
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10. Radar scanning geometry in 3D
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11. acceptable for convection research)
Kumpula August 2005
The task schedule consists of 5 subtasks, repeated every 10 minutes (10 minute interval longest
acceptable for convection research)
Task
Main purpose
Range/km
Elevations
/deg
Mode
Moments
PRF/Hz
Max wind
PRO_A
Good dBZ
150
FFT
Z, T, V,W, SQI
1000
13.3 m/s
PRO_B
Dual pol low part
PPP
ZDR, KDP, RhoHV, PhiDP
PRO_C
Dual pol upper air
120
As above
1200
16 m/s
D_PRO
Horizontal transmission,
H+V receiving
(for LDR)
(top to down)
Z,T,V,W,SQI
LDR, RhoH, PhiH
E_PRE
Dual PRF, 8-bit
(for mesocyclone winds)
Z, T, V, W
1200/ 800
32 m/s
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12. FMI tasks 1991-2006 Task Main purpose Range/km Elevations /deg Mode
The task schedule consists of 3 subtasks, repeated every 5 minutes during campaigns
Task
Main purpose
Range/km
Elevations
/deg
Mode
Moments
PRF/Hz
Max wind
VOL_A
Good dBZ
250
PPP
Z, T, V
570
7m/s
VOL_B
Middle part
120 4
5.5 8
As above
850/567 Hz
22 m/s
VOL_C
Upper air 80 13 25
1200/800
32 m/s
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13. Hydrometeor classification is not possible with dBZ only
DBZ rain hail snow sleet insects birds clutter
Overlap of hail and heavy rain
Overlap of snow and insects
Help from dual pol parameters
ZDR rain hail snow sleet insects birds clutter
-5 …
RHO rain hail snow sleet insects birds clutter
Draft
Draft
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14. Three ways to collect dual-pol data
Alternating H and V ”Old-fashioned mode”
Transmit H, receive H and V ”LDR mode”
Z,V and LDR Linear Depolarization Ratio
More sensitivity
Transmit H and V, receive H and V ”Star mode”
Z, V and ZDR, Rho, KdP, PhiDP
ZDR - Differential Reflectivity
Rho - Correlation Coefficient
PhiDP - Differential Phase
KDP - Specific Differential Phase
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15. ZDR=10Log(Zh/Zv) V %Zv %Zh H courtesy of Timo Puhakka, HU ZDR < 0
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16. ZDR=10Log(Zh/Zv) generally, for hydrometeors ZDR -3..+3 dB (ratio 1:2)
Increases with the sizes of liquid drops
Small with dry snow
Positive with horizontally oriented plate-crystals
Negative with vertically oriented ”needles”
Small or negative with hail
Indicates presence of frozen precipitation
Indicates super cooled water in updrafts
Indicates the onset of melting
With Zh can detect hail
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17. ZDR in showers, sea clutter and birds
Non-met
Weather
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18. Correlation coefficient rhv
Correlation coefficient = 1 for spheres and oriented spheroids
courtesy of Timo Puhakka, HU
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19. Decrease of correlation Rho indicates
Variety of hydrometeor types
Mixture of liquid and frozen hydrometeors (”Snöblandat regn”)
Hydrometeors with irregular shape
Wide distribution of hydrometeor orientation
Presence of large hail
Correlation coefficient <0.95 for hail, hail/rain mixture and for wet aggregates
courtesy of Timo Puhakka, HU
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20. RHO sea clutter and birds: pink > 0.94 precipitation
Inter-ference
Birds
Sea clutter
Anaprop
Showers
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21. RHO in elevation 7 deg - melting layer 0.94-0.99
Ice and snow
Melting snow
Water
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22. Linear Depolarization Ratio LDR
Shv=0
Shv=0
Shv > 0
courtesy of Timo Puhakka, HU
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23. Linear Depolarization Ratio LDR
Dry snow LDR<-30 dB
Rain LDR<-27 dB
Dry aggregates, small hail,graupel LDR<-20 dB
Wet aggregates, small hail,graupel -20<LDR<-10 dB
Hail, rain/hail mixture LDR>-20 dB
courtesy of Timo Puhakka, HU
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24. High resolution RHI’s of melting layer dBZ, Rho, LDR
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25. PhiDP The anisotropy of the medium leads to phase difference
between horizontal and vertical waves
(when horizontal waves go through more water)
The detection of this phase difference is the basis for PhiDP.
More often, range derivative of PhiDP known as KDP, is used.
courtesy of Timo Puhakka, HU
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26. Kdp example Attenuation ! Ilmatieteen laitos / PowerPoint ohjeistus

27. Attenuation visible in ZDR
horizontal waves more attenuated
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28. Datasets are huge - Recommended procedure
Select situation from Weather Diary
Browse ready-made images
Select and limit the dataset you want
Read readme.files
Get data
Process
Make conclusions
For reporting, consider whether you want to use ready-made images or draw your own
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29. Lake effect snow last Tuesday evening
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